AFL.automation.instrument.scatteringInterpolator

Classes

GenerateScattering([dataset, polytype])	This is the wrapper class that enables scattering interpolation within the classifier labels.
Scattering_generator([dataset, polytype, ...])	A class to interpolate the SAS data across processing and composition dimensions.

class AFL.automation.instrument.scatteringInterpolator.Scattering_generator(dataset=None, polytype=None, polydeg=20)

A class to interpolate the SAS data across processing and composition dimensions.

There are a few ways to do this, reducing the dimensionality with a polynomial fit has proved to be one good way to interpolate the GP. Other strategies are to fit a model of intensity as a function of Q values. One can incorporate the uncertainties in intensities in a Heteroscedastic way. (smearing of the dQ is harder to envision, but likely possible)

slay queen! generate that SAS (or spectroscopy)

__init__(dataset=None, polytype=None, polydeg=20)

standardize_data()

construct_model(kernel=None, noiseless=False, heteroscedastic=False)

train_model(kernel=None, optimizer=None, noiseless=False, heteroscedastic=False, tol=0.0001, niter=21)

generate_SAS(coords=[[0, 0, 0], [1, 2, 3]])

Returns the simulated scattering pattern given the specified coordinates and polynomial type if reduced

class AFL.automation.instrument.scatteringInterpolator.GenerateScattering(dataset=None, polytype=None)

This is the wrapper class that enables scattering interpolation within the classifier labels.

__init__(dataset=None, polytype=None)

train_all(kernel=None, optimizer=None, noiseless=True, heteroscedastic=False, niter=21, tol=0.0001)

is_in(coord=None)

Returns the index corresponding to the gp model where suggested coordinates are requested